> For the complete documentation index, see [llms.txt](https://jeyabalajis.gitbook.io/simple-rule-engine/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://jeyabalajis.gitbook.io/simple-rule-engine/examples.md).

# Examples

### A simple decision tree involving facts

#### Decision matrix

<table data-view="cards"><thead><tr><th>Bureau Score</th><th>Marital Status</th><th>Business Ownership</th><th>Decision</th></tr></thead><tbody><tr><td>between 650 and 800</td><td>in [Married, Unspecified]</td><td>in [Owned by Self, Owned by Family]</td><td><strong>GO</strong></td></tr></tbody></table>

#### Rule specification

```
from simpleruleengine.conditional.when_all import WhenAll
from simpleruleengine.expression.expression import Expression
from simpleruleengine.operator.between import Between
from simpleruleengine.operator.string_in import In
from simpleruleengine.rulerow.rule_row_decision import RuleRowDecision
from simpleruleengine.ruleset.rule_set_decision import RuleSetDecision
from simpleruleengine.token.numeric_token import NumericToken
from simpleruleengine.token.string_token import StringToken

if __name__ == "__main__":
    cibil_score_between_650_800 = Expression(
        NumericToken("cibil_score"),
        Between(floor=650, ceiling=800)
    )
    marital_status_in_married_unspecified = Expression(
        StringToken("marital_status"),
        In("Married", "Unspecified")
    )
    business_owned_by_self_family = Expression(
        StringToken("business_ownership"),
        In("Owned by Self", "Owned by Family")
    )

    rule_row_decision_go = RuleRowDecision(
        WhenAll(
            cibil_score_between_650_800,
            marital_status_in_married_unspecified,
            business_owned_by_self_family
        ),
        "GO"
    )
    rule_set_decision = RuleSetDecision(rule_row_decision_go)

    # Evaluate a fact set against this rule and assert the results.
    fact = dict(
        cibil_score=700,
        marital_status="Married",
        business_ownership="Owned by Self"
    )
    assert rule_set_decision.evaluate(fact) == "GO"
```

### A complex decision tree involving multiple AND and OR conditions

#### Decision matrix

<table data-view="cards"><thead><tr><th>Applicant Age</th><th>Applicant Ownership</th><th>Business Ownership</th><th>Decision</th></tr></thead><tbody><tr><td>>=35</td><td>in [Owned by Self, Owned by Family]</td><td>in [Owned by Self, Owned by Family]</td><td>GO</td></tr><tr><td>>=35</td><td>in [Owned by Self, Owned by Family]</td><td>in [Rented]</td><td>GO</td></tr><tr><td>>=35</td><td>in [Rented]</td><td>in [Owned by Self, Owned by Family]</td><td>GO</td></tr><tr><td>>=35</td><td>in [Rented]</td><td>in [Rented]</td><td>NO GO</td></tr><tr><td>&#x3C;35</td><td>in [Rented]</td><td>in [Rented]</td><td>NO GO</td></tr><tr><td>&#x3C;35</td><td>in [Owned by Self, Owned by Family]</td><td>in [Rented]</td><td>NO GO</td></tr><tr><td>&#x3C;35</td><td>in [Rented]</td><td>in [Owned by Self, Owned by Family]</td><td>NO GO</td></tr><tr><td>&#x3C;35</td><td>in [Owned by Self, Owned by Family]</td><td>in [Owned by Self, Owned by Family]</td><td>GO</td></tr></tbody></table>

* when the applicant age is >=35, either of applicant ownership or business ownership must be Owned.
* When the applicant age is <35, both the applicant ownership and business ownership must be Owned.

#### Rule specification

```
from simpleruleengine.conditional.when_all import WhenAll
from simpleruleengine.conditional.when_any import WhenAny
from simpleruleengine.expression.expression import Expression
from simpleruleengine.operator.greater_than_equal import Gte
from simpleruleengine.operator.string_in import In
from simpleruleengine.rulerow.rule_row_decision import RuleRowDecision
from simpleruleengine.ruleset.rule_set_decision import RuleSetDecision
from simpleruleengine.token.numeric_token import NumericToken
from simpleruleengine.token.string_token import StringToken

if __name__ == "__main__":
    applicant_age_gte_35 = Expression(
        NumericToken("applicant_age"),
        Gte(35)
    )
    business_owned_by_self_family = Expression(
        StringToken("business_ownership"),
        In("Owned by Self", "Owned by Family")
    )
    applicant_owned_by_self_family = Expression(
        StringToken("applicant_ownership"),
        In("Owned by Self", "Owned by Family")
    )

    rule_row_decision_go = RuleRowDecision(
        WhenAll(
            applicant_age_gte_35,
            WhenAny(
                business_owned_by_self_family,
                applicant_owned_by_self_family
            )
        ),
        "GO"
    )
    rule_set_decision = RuleSetDecision(rule_row_decision_go)

    fact_go = dict(
        applicant_age=42,
        applicant_ownership="Not Owned",
        business_ownership="Owned by Self"
    )
    assert rule_set_decision.evaluate(fact_go) == "GO"

    fact_no_go_1 = dict(
        applicant_age=42,
        applicant_ownership="Not Owned",
        business_ownership="Not Owned"
    )
    assert rule_set_decision.evaluate(fact_no_go_1) != "GO"

    fact_no_go_2 = dict(
        applicant_age=25,
        applicant_ownership="Owned by Self",
        business_ownership="Owned by Self"
    )
    assert rule_set_decision.evaluate(fact_no_go_2) != "GO"
```

### A scoring rule involving multiple parameters

|         Rule set Name         | Weightage |
| :---------------------------: | :-------: |
|    no\_of\_running\_bl\_pl    |    0.5    |
| last\_loan\_drawn\_in\_months |    0.5    |

#### no\_of\_running\_bl\_pl

|            Condition            | Score |
| :-----------------------------: | :---: |
|   no\_of\_running\_bl\_pl >= 7  |  -100 |
|   no\_of\_running\_bl\_pl >= 4  |  -40  |
|   no\_of\_running\_bl\_pl >= 2  |   30  |
|   no\_of\_running\_bl\_pl >= 0  |  100  |
| no\_of\_running\_bl\_pl is none |  100  |

#### last\_loan\_drawn\_in\_months

|               Condition               | Score |
| :-----------------------------------: | :---: |
|   last\_loan\_drawn\_in\_months == 0  |   30  |
|    last\_loan\_drawn\_in\_months <3   |  -30  |
|  last\_loan\_drawn\_in\_months <= 12  |   40  |
|   last\_loan\_drawn\_in\_months >12   |  100  |
| last\_loan\_drawn\_in\_months is none |  100  |

#### Rule Specification

```
from simpleruleengine.conditional.when_all import WhenAll
from simpleruleengine.operator.greater_than_equal import Gte
from simpleruleengine.operator.greater_than import Gt
from simpleruleengine.operator.equal import Eq
from simpleruleengine.operator.less_than import Lt
from simpleruleengine.operator.less_than_equal import Lte
from simpleruleengine.rulerow.rule_row_score import RuleRowScore
from simpleruleengine.ruleset.rule_set_score import RuleSetScore
from simpleruleengine.rule.rule_score import RuleScore
from simpleruleengine.token.numeric_token import NumericToken
from simpleruleengine.expression.expression import Expression

if __name__ == "__main__":
    no_run_bl_pl_gte_7_score_minus_100 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(7))),
        -100
    )
    no_run_bl_pl_gte_4_score_minus_40 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(4))),
        -40
    )
    no_run_bl_pl_gte_2_score_30 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(2))),
        30
    )
    no_run_bl_pl_gte_0_score_100 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(0))),
        100
    )

    no_of_run_bl_pl_rule_set = RuleSetScore(
        no_run_bl_pl_gte_7_score_minus_100,
        no_run_bl_pl_gte_4_score_minus_40,
        no_run_bl_pl_gte_2_score_30,
        no_run_bl_pl_gte_0_score_100,
        weight=0.5
    )

    fact_no_run_bl_pl_2 = dict(no_of_running_bl_pl=2)
    assert no_of_run_bl_pl_rule_set.evaluate(fact_no_run_bl_pl_2) == 15.0

    last_loan_drawn_in_months_eq_0_score_30 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Eq(0))),
        30
    )
    last_loan_drawn_in_months_lt_3_score_minus_30 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Lt(3))),
        -30
    )
    last_loan_drawn_in_months_lte_12_score_40 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Lte(12))),
        40
    )
    last_loan_drawn_in_months_gt_12_score_100 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Gt(12))),
        100
    )

    last_loan_drawn_in_months_rule_set = RuleSetScore(
        last_loan_drawn_in_months_eq_0_score_30,
        last_loan_drawn_in_months_lt_3_score_minus_30,
        last_loan_drawn_in_months_lte_12_score_40,
        last_loan_drawn_in_months_gt_12_score_100,
        weight=0.5
    )

    fact_last_loan_drawn_in_months_lte_12 = dict(last_loan_drawn_in_months=6)
    assert last_loan_drawn_in_months_rule_set.evaluate(
        fact_last_loan_drawn_in_months_lte_12) == 20.0

    fact_rule_score = dict(last_loan_drawn_in_months=6, no_of_running_bl_pl=2)
    rule_score = RuleScore(
        no_of_run_bl_pl_rule_set,
        last_loan_drawn_in_months_rule_set
    )
    assert rule_score.execute(fact_rule_score) == 35.0

    no_run_bl_pl_gte_7_score_minus_100 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(7))), -100)
    no_run_bl_pl_gte_4_score_minus_40 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(4))), -40)
    no_run_bl_pl_gte_2_score_30 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(2))), 30)
    no_run_bl_pl_gte_0_score_100 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(0))), 100)

    no_of_run_bl_pl_rule_set = RuleSetScore(
        no_run_bl_pl_gte_7_score_minus_100,
        no_run_bl_pl_gte_4_score_minus_40,
        no_run_bl_pl_gte_2_score_30,
        no_run_bl_pl_gte_0_score_100,
        weight=0.5
    )

    fact_no_run_bl_pl_2 = dict(no_of_running_bl_pl=2)
    assert no_of_run_bl_pl_rule_set.evaluate(fact_no_run_bl_pl_2) == 15.0

    last_loan_drawn_in_months_eq_0_score_30 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Eq(0))),
        30
    )
    last_loan_drawn_in_months_lt_3_score_minus_30 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Lt(3))),
        -30
    )
    last_loan_drawn_in_months_lte_12_score_40 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Lte(12))),
        40
    )
    last_loan_drawn_in_months_gt_12_score_100 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Gt(12))),
        100
    )

    last_loan_drawn_in_months_rule_set = RuleSetScore(
        last_loan_drawn_in_months_eq_0_score_30,
        last_loan_drawn_in_months_lt_3_score_minus_30,
        last_loan_drawn_in_months_lte_12_score_40,
        last_loan_drawn_in_months_gt_12_score_100,
        weight=0.5
    )

    fact_last_loan_drawn_in_months_lte_12 = dict(last_loan_drawn_in_months=6)
    assert last_loan_drawn_in_months_rule_set.evaluate(
        fact_last_loan_drawn_in_months_lte_12) == 20.0

    fact_rule_score = dict(last_loan_drawn_in_months=6, no_of_running_bl_pl=2)
    rule_score = RuleScore(
        no_of_run_bl_pl_rule_set,
        last_loan_drawn_in_months_rule_set
    )
    assert rule_score.execute(fact_rule_score) == 35.0
```

### A nested rule that involves another rule for evaulation

#### Rule

* If cibil score is between 650 and 800, score is 100
* If cibil score is less than 650, score is 0
* Decide GO if pet in \[dog, cat] and cibil score is greater than 0

#### Rule Specification

```
from simpleruleengine.conditional.when_all import WhenAll
from simpleruleengine.expression.expression import Expression
from simpleruleengine.operator.between import Between
from simpleruleengine.operator.greater_than import Gt
from simpleruleengine.operator.greater_than_equal import Gte
from simpleruleengine.operator.string_in import In
from simpleruleengine.operator.less_than_equal import Lte
from simpleruleengine.operator.less_than import Lt
from simpleruleengine.operator.string_not_in import NotIn
from simpleruleengine.rulerow.rule_row_decision import RuleRowDecision
from simpleruleengine.ruleset.rule_set_decision import RuleSetDecision
from simpleruleengine.rulerow.rule_row_score import RuleRowScore
from simpleruleengine.ruleset.rule_set_score import RuleSetScore
from simpleruleengine.token.numeric_token import NumericToken
from simpleruleengine.token.string_token import StringToken
from simpleruleengine.token.rule_token import RuleToken
from simpleruleengine.rule.rule_score import RuleScore

if __name__ == "__main__":
    cibil_score_between_650_800 = Expression(
        NumericToken("cibil_score"),
        Between(floor=650, ceiling=800)
    )

    cibil_score_lt_650 = Expression(
        NumericToken("cibil_score"),
        Lt(650)
    )

    rule_row_between_650_800 = RuleRowScore(
        antecedent=WhenAll(cibil_score_between_650_800),
        consequent=100
    )

    rule_row_between_lt_650 = RuleRowScore(
        antecedent=WhenAll(cibil_score_lt_650),
        consequent=0
    )

    rule_set_cibil_score = RuleSetScore(
        rule_row_between_lt_650,
        rule_row_between_650_800,
        weight=1
    )

    rule_cibil_score = RuleScore(rule_set_cibil_score)

    fact = dict(cibil_score=350)
    assert rule_cibil_score.execute(fact) == 0

    expression_pet_in_dog_cat = Expression(
        StringToken(name="pet"),
        In("dog", "cat")
    )

    expression_cibil_score_gt_0 = Expression(
        RuleToken(name="cibil_rule", rule=rule_cibil_score),
        Gt(0)
    )

    when_all_cibil_and_pet = WhenAll(
        expression_cibil_score_gt_0,
        expression_pet_in_dog_cat
    )

    rule_row_go_cibil_and_pet = RuleRowDecision(
        antecedent=when_all_cibil_and_pet,
        consequent="GO"
    )

    rule_set_decision_cibil_score_and_pet = RuleSetDecision(
        rule_row_go_cibil_and_pet)

    fact = dict(cibil_score=350, pet="dog")
    assert rule_set_decision_cibil_score_and_pet.evaluate(fact) != "GO"

    fact = dict(cibil_score=725, pet="dog")
    assert rule_set_decision_cibil_score_and_pet.evaluate(fact) == "GO"
```


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